



'''simple docstring'''



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase_  :							Union[str, Any]             =    logging.get_logger(__name__)

# General docstring
lowerCAmelCase_  :							Any             =    'RegNetConfig'

# Base docstring
lowerCAmelCase_  :							Optional[int]             =    'facebook/regnet-y-040'
lowerCAmelCase_  :							Optional[int]             =    [1, 1088, 7, 7]

# Image classification docstring
lowerCAmelCase_  :							List[str]             =    'facebook/regnet-y-040'
lowerCAmelCase_  :							Optional[Any]             =    'tabby, tabby cat'

lowerCAmelCase_  :							Dict             =    [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   Optional[Any]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int = 3					,     lowerCAmelCase__ :   int = 1					,     lowerCAmelCase__ :   int = 1					,     lowerCAmelCase__ :   Optional[str] = "relu"					,     ):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      nn.Convad(
													    lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=lowerCAmelCase__					,     stride=lowerCAmelCase__					,     padding=kernel_size // 2					,     groups=lowerCAmelCase__					,     bias=lowerCAmelCase__					,     )
													SCREAMING_SNAKE_CASE       :   str         =      nn.BatchNormad(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      ACTaFN[activation] if activation is not None else nn.Identity()







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.convolution(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.normalization(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.activation(lowerCAmelCase__					)
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   str					,     lowerCAmelCase__ :   RegNetConfig					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   str         =      RegNetConvLayer(
													    config.num_channels					,     config.embedding_size					,     kernel_size=3					,     stride=2					,     activation=config.hidden_act					)
													SCREAMING_SNAKE_CASE       :   Any         =      config.num_channels







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      pixel_values.shape[1]
													if num_channels != self.num_channels:
																				raise ValueError(
																				    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''					)
													SCREAMING_SNAKE_CASE       :   str         =      self.embedder(lowerCAmelCase__					)
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   List[Any]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int = 2					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      nn.Convad(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					,     stride=lowerCAmelCase__					,     bias=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      nn.BatchNormad(lowerCAmelCase__					)







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   Tensor					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.convolution(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.normalization(lowerCAmelCase__					)
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   Union[str, Any]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													super().__init__()

													SCREAMING_SNAKE_CASE       :   List[Any]         =      nn.AdaptiveAvgPoolad((1, 1)					)
													SCREAMING_SNAKE_CASE       :   Any         =      nn.Sequential(
													    nn.Convad(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					)					,     nn.ReLU()					,     nn.Convad(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					)					,     nn.Sigmoid()					,     )







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													# b c h w -> b c 1 1
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.pooler(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   str         =      self.attention(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   str         =      hidden_state * attention
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   int					,     lowerCAmelCase__ :   RegNetConfig					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int = 1					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_channels != out_channels or stride != 1
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      max(1					,     out_channels // config.groups_width					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      (
													    RegNetShortCut(lowerCAmelCase__					,     lowerCAmelCase__					,     stride=lowerCAmelCase__					) if should_apply_shortcut else nn.Identity()
													)
													SCREAMING_SNAKE_CASE       :   int         =      nn.Sequential(
													    RegNetConvLayer(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					,     activation=config.hidden_act					)					,     RegNetConvLayer(lowerCAmelCase__					,     lowerCAmelCase__					,     stride=lowerCAmelCase__					,     groups=lowerCAmelCase__					,     activation=config.hidden_act					)					,     RegNetConvLayer(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					,     activation=lowerCAmelCase__					)					,     )
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      ACTaFN[config.hidden_act]







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      hidden_state
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.layer(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.shortcut(lowerCAmelCase__					)
													hidden_state += residual
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.activation(lowerCAmelCase__					)
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   Any					,     lowerCAmelCase__ :   RegNetConfig					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int = 1					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_channels != out_channels or stride != 1
													SCREAMING_SNAKE_CASE       :   Tuple         =      max(1					,     out_channels // config.groups_width					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      (
													    RegNetShortCut(lowerCAmelCase__					,     lowerCAmelCase__					,     stride=lowerCAmelCase__					) if should_apply_shortcut else nn.Identity()
													)
													SCREAMING_SNAKE_CASE       :   str         =      nn.Sequential(
													    RegNetConvLayer(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					,     activation=config.hidden_act					)					,     RegNetConvLayer(lowerCAmelCase__					,     lowerCAmelCase__					,     stride=lowerCAmelCase__					,     groups=lowerCAmelCase__					,     activation=config.hidden_act					)					,     RegNetSELayer(lowerCAmelCase__					,     reduced_channels=int(round(in_channels / 4					)					)					)					,     RegNetConvLayer(lowerCAmelCase__					,     lowerCAmelCase__					,     kernel_size=1					,     activation=lowerCAmelCase__					)					,     )
													SCREAMING_SNAKE_CASE       :   Any         =      ACTaFN[config.hidden_act]







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      hidden_state
													SCREAMING_SNAKE_CASE       :   Dict         =      self.layer(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.shortcut(lowerCAmelCase__					)
													hidden_state += residual
													SCREAMING_SNAKE_CASE       :   Dict         =      self.activation(lowerCAmelCase__					)
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   Optional[Any]					,     lowerCAmelCase__ :   RegNetConfig					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   int = 2					,     lowerCAmelCase__ :   int = 2					,     ):

													"""simple docstring"""
													super().__init__()

													SCREAMING_SNAKE_CASE       :   int         =      RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      nn.Sequential(
													    # downsampling is done in the first layer with stride of 2
													    layer(
													        lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     stride=lowerCAmelCase__					,     )					,     *[layer(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					) for _ in range(depth - 1					)]					,     )







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.layers(lowerCAmelCase__					)
													return hidden_state


class 						lowerCamelCase_				(       nn.Module      ):







						def __init__(	self :   int					,     lowerCAmelCase__ :   RegNetConfig					):

													"""simple docstring"""
													super().__init__()
													SCREAMING_SNAKE_CASE       :   Any         =      nn.ModuleList([]					)
													# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
													self.stages.append(
													    RegNetStage(
													        lowerCAmelCase__					,     config.embedding_size					,     config.hidden_sizes[0]					,     stride=2 if config.downsample_in_first_stage else 1					,     depth=config.depths[0]					,     )					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      zip(config.hidden_sizes					,     config.hidden_sizes[1:]					)
													for (in_channels, out_channels), depth in zip(lowerCAmelCase__					,     config.depths[1:]					):
																				self.stages.append(RegNetStage(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     depth=lowerCAmelCase__					)					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   Tensor					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   bool = True					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      () if output_hidden_states else None

													for stage_module in self.stages:
																				if output_hidden_states:
																											SCREAMING_SNAKE_CASE       :   Tuple         =      hidden_states + (hidden_state,)

																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      stage_module(lowerCAmelCase__					)

													if output_hidden_states:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      hidden_states + (hidden_state,)

													if not return_dict:
																				return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

													return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__					,     hidden_states=lowerCAmelCase__					)


class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Dict						  =					RegNetConfig
						_lowerCAmelCase  : int						  =					'regnet'
						_lowerCAmelCase  : int						  =					'pixel_values'
						_lowerCAmelCase  : Tuple						  =					True







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													if isinstance(lowerCAmelCase__					,     nn.Convad					):
																				nn.init.kaiming_normal_(module.weight					,     mode='''fan_out'''					,     nonlinearity='''relu'''					)
													elif isinstance(lowerCAmelCase__					,     (nn.BatchNormad, nn.GroupNorm)					):
																				nn.init.constant_(module.weight					,     1					)
																				nn.init.constant_(module.bias					,     0					)







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   Optional[int]=False					):

													"""simple docstring"""
													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      value


lowerCAmelCase_  :							Optional[Any]             =    R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowerCAmelCase_  :							int             =    R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'     ,					snake_case_     ,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 						lowerCamelCase_				(       snake_case_      ):







						def __init__(	self :   str					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													super().__init__(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      config
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      RegNetEmbeddings(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Dict         =      RegNetEncoder(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      nn.AdaptiveAvgPoolad((1, 1)					)
													# Initialize weights and apply final processing
													self.post_init()







						@add_start_docstrings_to_model_forward(lowerCAmelCase__					)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC					,     output_type=lowerCAmelCase__					,     config_class=_CONFIG_FOR_DOC					,     modality='''vision'''					,     expected_output=_EXPECTED_OUTPUT_SHAPE					,     )
						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   Tensor					,     lowerCAmelCase__ :   Optional[bool] = None					,     lowerCAmelCase__ :   Optional[bool] = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      (
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													SCREAMING_SNAKE_CASE       :   int         =      return_dict if return_dict is not None else self.config.use_return_dict

													SCREAMING_SNAKE_CASE       :   str         =      self.embedder(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   List[str]         =      self.encoder(
													    lowerCAmelCase__					,     output_hidden_states=lowerCAmelCase__					,     return_dict=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      encoder_outputs[0]

													SCREAMING_SNAKE_CASE       :   int         =      self.pooler(lowerCAmelCase__					)

													if not return_dict:
																				return (last_hidden_state, pooled_output) + encoder_outputs[1:]

													return BaseModelOutputWithPoolingAndNoAttention(
													    last_hidden_state=lowerCAmelCase__					,     pooler_output=lowerCAmelCase__					,     hidden_states=encoder_outputs.hidden_states					,     )


@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '     ,					snake_case_     ,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 						lowerCamelCase_				(       snake_case_      ):







						def __init__(	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													super().__init__(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      config.num_labels
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      RegNetModel(lowerCAmelCase__					)
													# classification head
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      nn.Sequential(
													    nn.Flatten()					,     nn.Linear(config.hidden_sizes[-1]					,     config.num_labels					) if config.num_labels > 0 else nn.Identity()					,     )
													# initialize weights and apply final processing
													self.post_init()







						@add_start_docstrings_to_model_forward(lowerCAmelCase__					)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT					,     output_type=lowerCAmelCase__					,     config_class=_CONFIG_FOR_DOC					,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,     )
						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[torch.FloatTensor] = None					,     lowerCAmelCase__ :   Optional[torch.LongTensor] = None					,     lowerCAmelCase__ :   Optional[bool] = None					,     lowerCAmelCase__ :   Optional[bool] = None					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      return_dict if return_dict is not None else self.config.use_return_dict

													SCREAMING_SNAKE_CASE       :   Dict         =      self.regnet(lowerCAmelCase__					,     output_hidden_states=lowerCAmelCase__					,     return_dict=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      outputs.pooler_output if return_dict else outputs[1]

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.classifier(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   int         =      None

													if labels is not None:
																				if self.config.problem_type is None:
																											if self.num_labels == 1:
																																		SCREAMING_SNAKE_CASE       :   List[str]         =      '''regression'''
																											elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																		SCREAMING_SNAKE_CASE       :   str         =      '''single_label_classification'''
																											else:
																																		SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      '''multi_label_classification'''
																				if self.config.problem_type == "regression":
																											SCREAMING_SNAKE_CASE       :   Dict         =      MSELoss()
																											if self.num_labels == 1:
																																		SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      loss_fct(logits.squeeze()					,     labels.squeeze()					)
																											else:
																																		SCREAMING_SNAKE_CASE       :   List[Any]         =      loss_fct(lowerCAmelCase__					,     lowerCAmelCase__					)
																				elif self.config.problem_type == "single_label_classification":
																											SCREAMING_SNAKE_CASE       :   Tuple         =      CrossEntropyLoss()
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      loss_fct(logits.view(-1					,     self.num_labels					)					,     labels.view(-1					)					)
																				elif self.config.problem_type == "multi_label_classification":
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      BCEWithLogitsLoss()
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      loss_fct(lowerCAmelCase__					,     lowerCAmelCase__					)

													if not return_dict:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      (logits,) + outputs[2:]
																				return (loss,) + output if loss is not None else output

													return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__					,     logits=lowerCAmelCase__					,     hidden_states=outputs.hidden_states					)









'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




